Overview

Goals:

Part 1: Factor management

Overview of factors

Factors are “truly categorical” variables. They are vectors that:

We will use the gapminder dataset to explore working with factors in the following ways:

Load libraries

suppressPackageStartupMessages(library(tidyr))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(forcats))
suppressPackageStartupMessages(library(plotly))
suppressPackageStartupMessages(library(gapminder))

Look at the data

str(gapminder)
## Classes 'tbl_df', 'tbl' and 'data.frame':    1704 obs. of  6 variables:
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
##  $ pop      : int  8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num  779 821 853 836 740 ...

This confirms that we have two factors, country (142 levels), and continent (5 levels).

Drop Oceania

Suppose we wanted to remove observations in the continent of Oceania from the dataset.

To start, we should review the names of the level using the nlevels function:

levels(gapminder$continent)
## [1] "Africa"   "Americas" "Asia"     "Europe"   "Oceania"

Next we filter the data. We can remove unused levels at the same time by piping the filter results into the droplevels() function:

gap_sans_OCE <- gapminder %>% 
  filter(continent != "Oceania") %>% 
  droplevels()

Now let’s confirm there are no longer any “Oceania” rows:

NROW(gap_sans_OCE %>% 
  filter(continent == "Oceania"))
## [1] 0

Looks good! We can also check how many rows were dropped:

# Subtract to find difference
NROW(gapminder) - NROW(gap_sans_OCE)
## [1] 24

We have now confirmed that all “Oceania” rows (n=24) have been removed from the original dataset. What about the levels?

levels(gap_sans_OCE$continent)
## [1] "Africa"   "Americas" "Asia"     "Europe"

Great! We have successfully removed the unused levels.

Reorder the levels of country or continent

Suppose we wanted to plot mean life expectancy for each continent:

mean_pop <- gapminder %>%
  group_by(continent) %>% 
  summarize(mean.pop = mean(pop)) 
knitr::kable(mean_pop)
continent mean.pop
Africa 9916003
Americas 24504795
Asia 77038722
Europe 17169765
Oceania 8874672

Let’s plot the reordered data:

ggplot(mean_pop, aes(continent, mean.pop)) + 
  geom_point()

By default, the levels are ordered alphabetically, which does not help to highlight any patterns in the data.

We can change these to ascending order using the fct_reorder function:

mean_pop %>%
  mutate(continent = fct_reorder(continent, mean.pop)) %>% 
  ggplot(aes(continent, mean.pop)) + 
  geom_point()

Part 2: File I/O

Let’s practice exporting and importing data. Say we wanted to look at life expectancies in American countries with a population over 5,000,000 for the year 2007:

Filter and arrange data

America_pop_2007 <- gapminder %>%
  filter(continent == "Americas", year == 2007, pop > 5000000) %>%
  arrange(pop) # arrange in ascending order

Let’s view it as a table:

knitr::kable(America_pop_2007)
country continent year lifeExp pop gdpPercap
Nicaragua Americas 2007 72.899 5675356 2749.321
Paraguay Americas 2007 71.752 6667147 4172.838
El Salvador Americas 2007 71.878 6939688 5728.354
Honduras Americas 2007 70.198 7483763 3548.331
Haiti Americas 2007 60.916 8502814 1201.637
Bolivia Americas 2007 65.554 9119152 3822.137
Dominican Republic Americas 2007 72.235 9319622 6025.375
Cuba Americas 2007 78.273 11416987 8948.103
Guatemala Americas 2007 70.259 12572928 5186.050
Ecuador Americas 2007 74.994 13755680 6873.262
Chile Americas 2007 78.553 16284741 13171.639
Venezuela Americas 2007 73.747 26084662 11415.806
Peru Americas 2007 71.421 28674757 7408.906
Canada Americas 2007 80.653 33390141 36319.235
Argentina Americas 2007 75.320 40301927 12779.380
Colombia Americas 2007 72.889 44227550 7006.580
Mexico Americas 2007 76.195 108700891 11977.575
Brazil Americas 2007 72.390 190010647 9065.801
United States Americas 2007 78.242 301139947 42951.653

Write to .CSV

Let’s practice exporting to a comma separated values (.csv) file:

# set row.names to FALSE to avoide creation of extra ID column
write.csv(America_pop_2007, file = "America_pop_2007_5M.csv", row.names = FALSE)

We can see that the .csv was successfully exported to the project folder and the data are correct in Microsoft Excel.

Does its structure survive if we re-import it into R?

Re-import from .csv

America_pop_2007_READ <- read.csv("America_pop_2007_5M.csv")
knitr::kable(America_pop_2007_READ)
country continent year lifeExp pop gdpPercap
Nicaragua Americas 2007 72.899 5675356 2749.321
Paraguay Americas 2007 71.752 6667147 4172.838
El Salvador Americas 2007 71.878 6939688 5728.354
Honduras Americas 2007 70.198 7483763 3548.331
Haiti Americas 2007 60.916 8502814 1201.637
Bolivia Americas 2007 65.554 9119152 3822.137
Dominican Republic Americas 2007 72.235 9319622 6025.375
Cuba Americas 2007 78.273 11416987 8948.103
Guatemala Americas 2007 70.259 12572928 5186.050
Ecuador Americas 2007 74.994 13755680 6873.262
Chile Americas 2007 78.553 16284741 13171.639
Venezuela Americas 2007 73.747 26084662 11415.806
Peru Americas 2007 71.421 28674757 7408.906
Canada Americas 2007 80.653 33390141 36319.235
Argentina Americas 2007 75.320 40301927 12779.380
Colombia Americas 2007 72.889 44227550 7006.580
Mexico Americas 2007 76.195 108700891 11977.575
Brazil Americas 2007 72.390 190010647 9065.801
United States Americas 2007 78.242 301139947 42951.653
Great! The data are s till arrange d by in trecing po pulation, so it surved the write-out/read-in process.

Note: To write or read a .csv outside of the project folder, you need to enter in the full file path.

Part 3: Visualization design

In this section I reproduce an old figure and give it some new life through the techniques I’ve learned in the last couple of weeks of lecture.

Old Graph

Below is the code and graph (“as is”) for a figure I was very proud of from Homework 02:

old_graph <- select(gapminder, gdpPercap, lifeExp, pop, year, continent) %>% #subsetting data
  filter(year > 1957, continent=="Europe"|continent=="Africa") %>% #filter by criteria
  ggplot(aes(lifeExp, gdpPercap))+ #piped to ggplot
  geom_point(aes(color=continent, size=pop, alpha=0.1)) + # add aesthetics 
  xlab("Life Expectancy")+
  ylab("GDP per Capita")
old_graph

New Graph

new_graph <- select(gapminder, gdpPercap, lifeExp, pop, year, continent) %>% 
  filter(year > 1957, continent == "Europe"|continent == "Africa") %>%   ggplot(aes(lifeExp, gdpPercap)) +
  geom_point(aes(color = continent, size = pop, alpha = 0.1)) +
  labs(title = "Life expectancy for each continent", 
       x = "Life Expectancy", 
       y = "GDP per Capita") + 
  theme_bw()+
  theme(axis.text.x  = element_text(vjust=0.6, size=10),
        axis.text = element_text(size = 10)) +
  scale_colour_brewer(palette = "Set1") + # use brewer scheme
  geom_smooth(method="lm") #add a linear regression line
new_graph

Export to plotly

new_graph_plotly <- new_graph
ggplotly(new_graph_plotly)

Reflections

One of the first changes I made was to remove the unnecessary comments, as these did not provide extra information and make the code difficult to read, violating the recommendations by Hadley Wickham in the tidyverse style guide. Also in line with the style guide, I added spaces before and after each of the “=” and “+” in order to improve legibility.

I also added a linear regression line, which is particularly nice to include on the plotly plot, as it allows you to identify the predicted values from the model interactively. I could see this–along with the fact that plotly provide access to the specific attribute information for each point–being extremely useful for exploring and presenting data in the future.

Part 4: Writing figures to file

With ggsave() it’s easy to export a figure to as a file on your local computer:

ggsave("new_graph.png", new_graph)
## Saving 7 x 5 in image

We also may want to adjust the dimensions and resolution:

ggsave("new_graph_5x4.png", width = 5, height = 4, dpi = 150)

We can also save to vector format with .pdf

ggsave("new_graph.pdf", new_graph)
## Saving 7 x 5 in image

The ggsave( ) function will, by default, save the most recent plot in R. To save a different image, we have to make this explicit in the ggsave code. For example, if we wanted to save the old graph (see above), we would make the following changes to the code:

ggsave("old_graph.png", plot = old_graph)
## Saving 7 x 5 in image